Artificial intelligence has gone through some dismal periods, which those in the field gloomily refer to as "AI winters." This is not one of those times; in fact, AI is so hot right now that tech giants like Google, Facebook, Apple, Baidu, and Microsoft are battling for the leading minds in the field. The current excitement about AI stems, in great part, from groundbreaking advances involving what are known as "convolutional neural networks." This machine learning technique promises dramatic improvements in things like computer vision, speech recognition, and natural language processing. You probably have heard of it by its more layperson-friendly name: "Deep Learning." Few people have been more closely associated with Deep Learning than Yann LeCun, 54. Working as a Bell Labs researcher during the late 1980s, LeCun developed the convolutional network technique and showed how it could be used to significantly improve handwriting recognition; many of the checks written in the United States are now processed with his approach. Between the mid-1990s and the late 2000s, when neural networks had fallen out of favor, LeCun was one of a handful of scientists who persevered with them. He became a professor at New York University in 2003, and has since spearheaded many other Deep Learning advances. More recently, Deep Learning and its related fields grew to become one of the most active areas in computer research. Which is one reason that at the end of 2013, LeCun was appointed head of the newly-created Artificial Intelligence Research Lab at Facebook, though he continues with his NYU duties. LeCun was born in France, and retains from his native country a sense of the importance of the role of the "public intellectual." He writes and speaks frequently in his technical areas, of course, but is also not afraid to opine outside his field, including about current events. IEEE Spectrum contributor Lee Gomes spoke with LeCun at his Facebook office in New York City. The following has been edited and condensed for clarity. IEEE Spectrum: We read about Deep Learning in the news a lot these days.

NEW DELHI: Artificial intelligence (AI) or machine intelligence has always been a little scary. We picture evil robots controlling the world and making human beings obsolete or, even worse, using us as energy sources as in the Matrix. The defeat by Google's DeepMind – a computer program – of the world champion in Go, an ancient Chinese board game, has reinforced the apocalyptic vision of machines taking over the world in the popular media. Not that this vision is totally wrong. The more we transfer human skills to the machine, the more obsolescence in the work force.

What was once thought to be good news has turned to bad after the artificial intelligence (AI) as it has been discovered that the system for automated banking has been taking money from customers. Massachusetts Institute of Technology scientist Len Meha-Dohler stated that this was a nightmare although they had not involvement with the project. The system called Deep Learning Interface for Accounting – or Delia for short- held the money in a separate account according to Stanford Universities Rob Ott. He was involved and believes the money would have been returned. After the recent event where DeepMinds program beat a chess expert at the game, it was considered that AI was the way forward.

By 2020, a quarter of Europeans will be over 60. In their silver age, many would like to stay in their homes and will require care from family or social workers. Unfortunately, the number of caregivers is diminishing year-after-year due to shifting demographics and an increase in working families. This leads to a'care deficit' that poses a major challenge to most European societies. And today's social workers are often hard pressed, wishing they had more time to connect with the people they care for, rather than the minuted dance of tasks that need to be done.

So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together - we know it took us a while. This article is an introduction we wish we had back then. While we have some grasp on the matter, we're not experts, so the following might contain inaccuracies or even outright errors. Feel free to point them out, either in the comments or privately.

We derive generalized estimators for a number of spatial statistics that have been used in the analysis of spatially resolved omics data, such as Ripley's K, H and L functions, clustering index, and degree of clustering, which allow these statistics to be calculated on data modelled by arbitrary random measures (RMs). Our estimators generalize those typically used to calculate these statistics on point process data, allowing them to be calculated on RMs which assign continuous values to spatial regions, for instance to model protein intensity. The clustering index (H*) compares Ripley's H function calculated empirically to its distribution under complete spatial randomness (CSR), leading us to consider CSR null hypotheses for RMs which are not point-processes when generalizing this statistic. We thus consider restricted classes of completely random measures which can be simulated directly (Gamma processes and Marked Poisson Processes), as well as the general class of all CSR RMs, for which we derive an exact permutation-based H* estimator. We establish several properties of the estimators, including bounds on the accuracy of our general Ripley K estimator, its relationship to a previous estimator for the cross-correlation measure, and the relationship of our generalized H* estimator to previous statistics. To test the ability of our approach to identify spatial patterning, we use Fluorescent In Situ Hybridization (FISH) and Immunofluorescence (IF) data to probe for mRNA and protein subcellular localization patterns respectively in polarizing mouse fibroblasts on micropattened cells. We observe correlated patterns of clustering over time for corresponding mRNAs and proteins, suggesting a deterministic effect of mRNA localization on protein localization for several pairs tested, including one case in which spatial patterning at the mRNA level has not been previously demonstrated.

We propose a novel method to determine the dissimilarity between subjects for functional data clustering. Spline smoothing or interpolation is common to deal with data of such type. Instead of estimating the best-representing curve for each subject as fixed during clustering, we measure the dissimilarity between subjects based on varying curve estimates with commutation of smoothing parameters pair-by-pair (of subjects). The intuitions are that smoothing parameters of smoothing splines reflect inverse signal-to-noise ratios and that applying an identical smoothing parameter the smoothed curves for two similar subjects are expected to be close. The effectiveness of our proposal is shown through simulations comparing to other dissimilarity measures. It also has several pragmatic advantages. First, missing values or irregular time points can be handled directly, thanks to the nature of smoothing splines. Second, conventional clustering method based on dissimilarity can be employed straightforward, and the dissimilarity also serves as a useful tool for outlier detection. Third, the implementation is almost handy since subroutines for smoothing splines and numerical integration are widely available. Fourth, the computational complexity does not increase and is parallel with that in calculating Euclidean distance between curves estimated by smoothing splines.

Coregulation of the expression of groups of genes has been extensively demonstrated empirically in bacterial and eukaryotic systems. Such coregulation can arise through the use of shared regulatory motifs, which allow the coordinated expression of modules (and module groups) of functionally related genes across the genome. Coregulation can also arise through the physical association of multi-gene complexes through chromosomal looping, which are then transcribed together. We present a general formalism for modeling coregulation rules in the framework of Random Boolean Networks (RBN), and develop specific models for transcription factor networks with modular structure (including module groups, and multi-input modules (MIM) with autoregulation) and multi-gene complexes (including hierarchical differentiation between multi-gene complex members). We develop a mean-field approach to analyse the stability of large networks incorporating coregulation, and show that autoregulated MIM and hierarchical gene-complex models can achieve greater stability than networks without coregulation whose rules have matching activation frequency. We provide further analysis of the stability of small networks of both kinds through simulations. We also characterize several general properties of the transients and attractors in the hierarchical coregulation model, and show using simulations that the steady-state distribution factorizes hierarchically as a Bayesian network in a Markov Jump Process analogue of the RBN model.

Darktrace, the leader in Enterprise Immune System technology, presented a radical vision of cyber defense at InfoSec World 2016, Orlando, yesterday, where'immune system'-inspired technology can automatically find and respond to evolving cyber-threats. IT Security Architect at Steelcase, Stuart Berman, joined Sean O'Connor, Director at Darktrace on the conference stage as a guest speaker, to discuss how enterprises can tackle the cyber security challenges of tomorrow. As one of the world's leading manufacturers of corporate office environments, Steelcase is known for embracing new technology and innovation, and was quick to recognize the importance of adopting new models of security. Speaking at the InfoSec World Conference in Florida yesterday, Stuart Berman, who has over 20 years' experience in information security, shared his views on the future of cyber defense. "Math and machine learning are an important part of advanced threat defense, in the context of today's fast-moving, distributed work environments," Berman commented.

About these poems: Self Published Amazon Poems By Marco Essomba (@marcoessomba) a Network and Security Expert, Self Confessed Geek, CTO/Cofounder of AMPS Intl, a leading UK based Application Delivery Infrastructures (ADI) Solutions Provider with niche expertise in the world's most advanced ADCs (www.amps-global.com). This poem is part of a collection of encrypted poems for which only the author has the keys. But for those who are brave, a long journey to attempt to decrypt the new digital transformation and information technology related topics based on the author real life experience in the network and security field and career spanning more than 10 years. In the spirit of open source poetry, all feedback (including bad ones) will be much appreciated. Keep reading those poems, for you may learn something about life on earth.